class_docstr: The DBP15K dataset from the
  `"Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding"
  <https://arxiv.org/abs/1708.05045>`_ paper, where Chinese, Japanese and
  French versions of DBpedia were linked to its English version.
  Node features are given by pre-trained and aligned monolingual word
  embeddings from the `"Cross-lingual Knowledge Graph Alignment via Graph
  Matching Neural Network" <https://arxiv.org/abs/1905.11605>`_ paper.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      pair (str): The pair of languages (:obj:`"en_zh"`, :obj:`"en_fr"`,
          :obj:`"en_ja"`, :obj:`"zh_en"`, :obj:`"fr_en"`, :obj:`"ja_en"`).
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
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code: def __init__(
      self,
      root: str,
      pair: str,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      force_reload: bool = False,
  ) -> None:
      assert pair in [
          "en_zh",
          "en_fr",
          "en_ja",
          "zh_en",
          "fr_en",
          "ja_en",
      ]
      self.pair = pair
      super().__init__(
          root, transform, pre_transform, force_reload=force_reload
      )
      self.load(self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/dbp15k.py
imports: import os
  import os.path
  from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import fs, read_txt_array
  from torch_geometric.utils import sort_edge_index
label_desc: Use image node_id 1 to create a new DBP15K object from inherited base classes: InMemoryDataset with example: obj = DBP15K(root, pair, transform, pre_transform, force_reload)